• 자원환경지질
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• 제38권1호
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• pp.33-43
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• 2005

본 연구의 목적은 2002년 산사태가 많이 발생한 강원도 강릉 지역의 산사태 발생원인에 대해 인공신경망 기법과 GIS를 이용하여 취약성도를 작성 및 이를 검증하는 것이다. 이를 위해 지형도, 토양도, 임상도, 지질도, 토지피복도 등 을 GIS를 이용하여 공간 데이터베이스로 구축하였고, 이러한 데이터베이스로부터, 경사, 경사방향, 곡률, 수계, 지형종 류, 토질, 토양모재, 토양배수, 유효토심, 임상종류, 임상경급, 임상영급, 임상밀도, 암상, 토지피복도, 선구조도 등을 추 출하여 산사태 발생요인으로 이용하였다. 이러한 데이터베이스와 산사태 발생 위치에 대해 인공신경망 기법을 적용하 여 산사태 발생 원인에 대해 상대적인 가중치를 계산하고, 이를 적용하여 산사태 취약성도를 만들었다. 그리고 계산 된 산사태 취약성도는 산사태 발생을 정량적으로 예측하는 비곡선 방법을 이용하여 검증되었다. 이러한 결과는 산사 태 피해 예방을 위한 방재 사업, 국토개발 계획, 건설계획 등에 기초 자료로서 활용될 수 있다.

• 한국지반공학회논문집
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• 제19권2호
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• pp.267-278
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• 2003

인공신경망은 복잡한 문제를 해결하는데 있어 여러 분야에서 널리 활용되고 있는 매우 효과적인 기법으로 알려져 있다. 본 연구에서는 터널거동을 효과적으로 예측하기 위해 이러한 인공신경망 기법을 이용한 터널거동 예측시스템 (TBPS)을 개발하였다. 본 시스템 개발을 위해 시공이 완료된 31개 현장 193 개소 지점으로부터 얻은 터널 계측자료 (즉, 천단침하, 내공변위, 록볼트 축력, 숏크리트 압축 및 전단응력, 내진시 라이닝의 응력 등)를 D/B화하여 이용하였다. 또한 개발된 TBPS의 학습을 위해 가장 효과적이라 알려진 역전파 알고리즘을 사용하여 이들 자료의 학습을 실시하였다. 이러한 과정을 통해 개발된 TBPS를 이용하여 예측한 터널 거동 값과 현장계측 값, 수치해석에 의한 결과 값의 상호 비교 분석을 실시하였다. 비교분석 결과, TBPS에 의한 거동예측결과 값의 변화는 실무에 적용 가능한 범주에 있는 것으로 나타났다. 따라서 본 연구에서 개발된 TBPS는 터널의 타당성검토나 기본 및 실시설계 등에 적용하여 효율적으로 필요한 정보를 신속하게 얻을 수 있는데 사용될 수 있을 것으로 판단된다.

• 동력기계공학회지
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• 제7권2호
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• 한국정밀공학회지
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• 제28권7호
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• pp.827-833
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• 2011

Precision forging of gears has a lot of advantages when compared to conventional gear shaping, because it allows the manufacture of gear parts without flash and consequently without the need for subsequent machining operations. In this study, the cold forging process is determined to manufacture the cold forged product for the precision clutch gear used of a commercial automobile, To do this, shape ratio of initial shape having influence the forgeability of forged product is analyzed. The optimal initial shape of clutch gear is designed using the results of DEFORM-3D and the artificial neural network (ANN). The initial shape through the detail analysis results, such as metal flow, distributions of strain can be obtained.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1997년도 춘계학술대회 논문집
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• pp.99-102
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• 1997

An electronic nose system is an instrument designed far mimicking human olfactory system. It consists generally of gas (odor) sensor array corresponding to olfactory receptors of human nose and artificial neural network pattern recognition technique based on human biological odor sensing mechanism. Considerable attempts to develop the electronic nose system have been made far applications in the fields of floods, drinks, cosmetics, environment monitoring, etc. A portable electronic nose system has been fabricated by using oxide semiconductor gas sensor array and pattern recognition technique such as principal component analysis (PCA) and back propagation artificial neural network The sensor array consists of six thick film gas sensors whose sensing layers are Pd-doped WO$_3$ Pt-doped SnO$_2$ TiO$_2$-Sb$_2$O$_3$-Pd-doped SnO$_2$ TiO$_2$-Sb$_2$O$_{5}$-Pd-doped SnO$_2$+Pd filter layer, A1$_2$O$_3$-doped ZnO and PdCl$_2$-doped SnO$_2$. As an application the system has been used to identify CO/HC car exhausting gases and the identification has been successfully demonstrated.d.

• 소성∙가공
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• 제29권2호
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• 한국정밀공학회지
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• 제30권10호
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• pp.1079-1086
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• 2013

Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• Advances in Computational Design
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• 제7권3호
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• Tribology and Lubricants
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• 제38권4호
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• pp.152-161
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• 2022

Chemical mechanical planarization (CMP) is a technology that planarizes the surfaces of semiconductor devices using chemical reaction and mechanical material removal, and it is an essential process in manufacturing highly integrated semiconductors. In the CMP process, a conditioning process using a diamond conditioner is applied to remove by-products generated during processing and ensure the surface roughness of the CMP pad. In previous studies, prediction of pad wear by CMP conditioning has depended on numerical analysis studies based on mathematical simulation. In this study, using an artificial neural network, the ratio of conditioner coverage to the distance between centers in the conditioning system is input, and the average conditioning density, standard deviation, nonuniformity (NU), and conditioning density distribution are trained as targets. The result of training seems to predict the target data well, although the average conditioning density, standard deviation, and NU in the contact area of wafer and pad and all areas of the pad have some errors. In addition, in the case of NU, the prediction calculated from the training results of the average conditioning density and standard deviation can reduce the error of training compared with the results predicted through training. The results of training on the conditioning density profile generally follow the target data well, confirming that the shape of the conditioning density profile can be predicted.

• Structural Engineering and Mechanics
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• 제87권6호
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• pp.517-527
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• 2023

The severe deterioration of structures has led to extensive research on the development of structural repair techniques using composite materials. Consequently, previous researchers have devised various analytical methods to predict the interface performance of bonded repairs. However, these analytical solutions are highly complex mathematically and necessitate numerous calculations with a large number of iterations to obtain the output parameters. In this paper, an artificial neural network prediction models is used to calculate the interfacial stress distribution in RC beams strengthened with FRP sheet. The R2value for the training data is evaluated as 0.99, and for the testing data, it is 0.92. Closed-form solutions are derived for RC beams strengthened with composite sheets simply supported at both ends and verified through direct comparisons with existing results. A comparative study of peak interfacial shear and normal stresses with the literature gives the usefulness and effectiveness of ANN proposed. A parametrical study is carried out to show the effects of some design variables, e.g., thickness of adhesive layer and FRP sheet.